Advanced Analytics White Papers
See the most recent advanced analytics whitepapers below.
Find out more about how your organization can post a paper.
03/10/10
Designed for the marketing operations manager and executive, this series of Sirius Decisions research briefs focuses on the emergence of Marketing Operations and its increasing adoption of demand and sales funnel analytics to drive best-in-class performance and deliver competitive differentiators.
Sponsored By IBM_Cognos
03/10/10
This report examines how organizations are using data to impact marketing performance and marketing
effectiveness. For example, VITAS has been using IBM Cognos TM1 for over 10 years for everything from
complex expansion planning to health care regulatory compliance, realizing numerous benefits.
Read how in this case study.
Sponsored By IBM_Cognos
03/03/10
Many banks and financial services organizations use MicroStrategy Dynamic Enterprise Dashboards to improve the level of service they provide to account holders. These companies develop BI applications in five distinct areas: Customer Analysis, Operations and Financial Analysis, Sales and Marketing Analysis, Product Analysis, and Risk and Fraud Analysis. This white paper illustrates the types of dashboards possible for the financial services industry.
Sponsored By MicroStrategy
03/02/10
Aster Data's nCluster brings a forward-looking, exciting new design for analytic data requirements. With its massively parallel architecture; its use of commodity hardware; its focus on scalability, availability and manageability; and its rapid innovation via the integration of MapReduce and other features, Aster Data's nCluster offers users a distinctive set of capabilities in a promising new design.
Sponsored By Aster Data Systems
03/02/10
Visionary organizations are adopting an emerging practice known as “in-database analytics,” which supports more pervasive embedding of predictive models in business processes and mission-critical applications. With in-database analytics, enterprises migrate their predictive analysis (PA), data mining (DM), and other compute-intensive analytic functions from separate, standalone applications to execute in the enterprise data warehouse (EDW). Doing so allows IT professionals to leverage the EDW’s full parallel-processing, scalability, and optimization features. In-database analytics can help enterprises cut costs, speed development, and tighten governance on advanced analytics initiatives. Business process and applications (BP&A) professionals should implement in-database analytics in conjunction with ongoing efforts to consolidate and scale their EDW.
Sponsored By Aster Data Systems
02/04/10
Dramatically reducing the time, cost and effort required for integrating large amounts of Web data can radically simplify an organization’s ability to analyze online visitor behavior via a clickstream data warehouse (CDW). Learn how to optimize your CDW and:
• Gain greater insight into online customer behavior
• Make more strategic decisions based on actionable data
• Increase margins, lower costs and improve bottom line
• Increase staff productivity
Sponsored By Syncsort
01/25/10
When reviewing the popular literature on customer data integration and master data management, a frequently recurring business driver is the ability to establish a “360-degree view of the customer.” This concept has turned into the holy grail of comprehensive customer intelligence. This paper will provide a list of questions that should be considered and reviewed during any project designed to deliver a 360-degree view. By using these questions for preliminary guidance, one may clearly articulate where the business can derive value from the 360-degree view, determine performance metrics and thresholds, and improve the chances of success. The paper then explores how information sharing and identity resolution combine to show the connection between MDM and enterprise customer-centricity.
Sponsored By DataFlux
01/06/10
Enterprise BI environments help ensure a single version of the truth and a low long-term cost of ownership, while providing high performance, scalability, and rapid application development. However, achieving enterprise BI can take several months or years, and in the meantime, departments need access to fast, reliable BI. As a result, small departmental "islands" of BI surface throughout an organization, designed to satisfy immediate business needs. Although convenient, these applications lead to multiple versions of the truth and incur high costs of ownership. The solution, therefore, is to gradually consolidate departmental BI applications into a cohesive enterprise BI environment.
Sponsored By MicroStrategy
01/06/10
This white paper describes the purpose and benefits of both GIS and BI, the technological advancements that have fostered their integration, and the synergistic benefits of integrated applications that can benefit the entire organization without disrupting existing IT environments.
Sponsored By ESRI
11/17/09
We are entering a new period in the era of business Information systems. For most of this five-decade period (beginning in the mid-1950s), the primary focus has been on automating core business processes. The era began with custom-developed narrow-purpose applications and concluded with broad enterprise system packages provided by external vendors, but the purpose was the same: develop control and efficiency over processes by automating and capturing information from key business transactions. Whether a general ledger entry, a customer order captured, or a vacation balance debited, the transaction has been the primary unit around which this world revolved. By now, however, we have largely won the transaction war, and a new front is necessary in the war to manage information. While mop-up and maintenance activity on the transaction and application fronts are still necessary, most large organizations have the basic application functionality. The study focuses on the relationship between information and decision-making obtained from interviews with a number of organizations.
Sponsored By IBM